Portable Multi-Hypothesis Monte Carlo Localization for Mobile Robots

TL;DR

This paper introduces a portable multi-hypothesis Monte Carlo Localization method for mobile robots that maintains multiple particle populations, improving localization accuracy and recovery times compared to existing solutions.

Contribution

It presents a novel multi-hypothesis MCL approach with multi-scale matching and reliability metrics, enhancing localization robustness and integration with ROS2 Nav2.

Findings

Achieves improved localization accuracy over state-of-the-art methods.

Reduces recovery time from erroneous estimates or unknown initial positions.

Successfully integrated and tested within ROS2 Nav2 environment.

Abstract

Self-localization is a fundamental capability that mobile robot navigation systems integrate to move from one point to another using a map. Thus, any enhancement in localization accuracy is crucial to perform delicate dexterity tasks. This paper describes a new location that maintains several populations of particles using the Monte Carlo Localization (MCL) algorithm, always choosing the best one as the sytems's output. As novelties, our work includes a multi-scale match matching algorithm to create new MCL populations and a metric to determine the most reliable. It also contributes the state-of-the-art implementations, enhancing recovery times from erroneous estimates or unknown initial positions. The proposed method is evaluated in ROS2 in a module fully integrated with Nav2 and compared with the current state-of-the-art Adaptive ACML solution, obtaining good accuracy and recovery times.